Introduction to High-Dimensional Statistics

Introduction to High-Dimensional Statistics

Christophe Giraud

Chapman & Hall/CRC, 2015, xv + 255 pages, Price Unknown, hardcover

ISBN: 978-1-4822-3794-8

Readership: Statistics graduate students and researchers, as well as practitioners.

This is an attractive textbook. It will prove a very useful addition to any library or personal reference collection.

Technological advancements over the last 20 years or so have resulted in the proliferation of high-throughput datasets in many fields, such as biotechnology, finance and astrophysics. The data produced by such technology are said to be high-dimensional, and give rise to the need for new statistical thinking and methodology. This book achieves well what it sets out to provide, an introduction to the mathematical foundations of high-dimensional statistics. It is targeted at advanced students and researchers seeking an introduction to the area and to the key mathematics involved. It is not intended as a comprehensive account of contemporary statistical methods for analysis of high-dimensional data, and, as such, is likely to stand the tests of time well.

Formally, a statistical model is said to be high-dimensional if its number of parameters p grows with the number of observations n the model has to explain. Conventional statistical theory describes the asymptotic behaviour of procedures for data analysis when n goes to infinity with p fixed, which now makes no sense. A different paradigm is required, to provide a theory which is valid for any value of n and p. This is achieved by replacement of the convergence theorems used in conventional statistics by more elaborate technical arguments based on `concentration inequalities’, which are the central tools for the non asymptotic analysis of statistical methods in the high-dimensional setting.

Chapter 1 of the text provides a very lucid and accessible introduction to the key ideas and mathematical concepts involved in the analysis of high-dimensional data. To overcome the difficulties imposed by high dimensionality, the key is to exploit low-dimensional structure hidden in data, the saving assumption of sparsity, that many of the p parameters might be negligible in magnitude relative to the others. Model selection presents the theory for tackling identification of low-dimensional structure. Chapter 2 considers the key setting of the Gaussian regression model. The general idea is to compare different statistical models, corresponding to different hidden structures, and then select that most suited for estimation on the given data. An alternative to model selection, the idea of model aggregation instead of selecting one from among them, is described in Chapter 3. Both model selection and model aggregation are computationally prohibitive in many settings. Chapter 4 discusses the powerful strategy that has been successfully applied to circumvent computational difficulties, that of deriving estimators by efficient minimization of a convenient convex criterion: the Lasso estimator is the most widely celebrated example of such a procedure. The operational difficulty with this strategy is that some sort of selection of the estimator to be applied, and also typically choice of some tuning parameter, is required. Chapter 5 provides a thorough account of how to decide among different estimation schemes and different tuning parameters.

Chapters 6 and 7 crank up the levels of statistical sophistication. Chapter 6 considers multivariate regression, with special focus on the situation where measurements lie in the vicinity of some (unknown) low-dimensional space. The essential notion is that correlations between statistical problems can be exploited to improve the accuracy of statistical procedures. Chapter 7 focuses on the specific problem of simultaneous estimation of the conditional dependencies among a large set of variables, using the theory of graphical models.

The final two chapters consider issues arising in high-dimensional data classification. Chapter 8 is concerned with the mathematical foundations of procedures for multiple testing, with special consideration of control of the False Discovery Rate. The theory and key methods of supervised classification are discussed in Chapter 9. A very well structured and clear series of mathematical appendices conclude a very substantial and authoritative account. Each chapter contains detailed exercises, with collaborative solutions on a wikisite, though at the time of writing this review contents of the site are rather sparse.

This is a dense text, which demands close study: it is not necessarily a book for dipping into. However, the technicalities are presented cleanly and comprehensibly. Simple but clear practical illustrations are effective, and prevent the treatment from becoming dry. A natural comparison can be drawn with the textbook of Bühlmann and van de Geer (2011), which covers much the same ground, in an equally authoritative and accessible manner, and which, rather surprisingly, is not referenced here.

G. Alastair Young:

Department of Mathematics

Imperial College London

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London SW7 2AZ

U.K.

Reference

Bühlmann, P. and van de Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer.